‘We don’t know nearly enough’: an online survey exploring perspectives of specialists who support children with brain-based visual impairments
Bibliographic record
Abstract
Introduction: Children with brain-based visual impairments (some of whom have a diagnosis of Cerebral Visual Impairment, or 'CVI') represent a growing and underserved population within vision services. These children often have more complex needs than those with ocular visual impairments and benefit from specialist support from multiple disciplines. This study aimed to understand the perspectives of these specialists in terms of their goals, views on collaboration, and understanding of the term 'CVI'. Methods: We invited a range of specialists who work with children with brain-based visual impairments, including educators, rehabilitation staff, clinicians, and family members, to complete an online survey between April 2023 and April 2024. Results: The analysis included 94 respondents: 51 educators, 30 rehabilitation staff, 7 clinicians, and 6 family members. Respondents shared common goals of connecting with the child (87/94, 93%) and fostering their learning and development (82/94, 93%). However, respondents also noted some specific and divergent goals, which can be at odds with each other. Professional staff frequently identified family members as the most valuable source of information about their child's vision (36/88, 41%), though family members expressed feeling under-valued. Transdisciplinary clinics were highlighted as a helpful model to provide quality child-centered care. Of the 73 professional staff who reported being familiar with the term 'CVI' (73/88, 83%), most (61/73, 84%) thought it was underdiagnosed, but respondents had different perspectives on what a diagnosis meant. Only 73% of professionals familiar with CVI reported receiving formal training about it. Discussion: The varied goals and different perspectives on CVI create challenges to providing cohesive support for children with brain-based visual impairments. Increasing the availability of complementary formal training across disciplines and adopting transdisciplinary models of care are promising approaches to improve the quality of services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".